CN115577909A - Scheduling method of park integrated energy system considering price-based demand response and V2G - Google Patents

Abstract

The invention discloses a campus comprehensive energy system scheduling method considering price type demand response and V2G, which comprises the steps of firstly, establishing a V2G model, a combined heat and power demand response model and a carbon transaction model; then, considering electric power/natural gas/heat balance constraint, operation constraint and main network exchange power constraint, and aiming at maximizing social welfare of the park, establishing a low-carbon economic deterministic day-ahead scheduling model of the park integrated energy system; and finally, considering the uncertainty of wind power/photovoltaic output and the uncertainty of electric/thermal load, providing a two-stage robust optimization scheduling model, and solving by using a dual transformation, an extreme point method and a CCG method. Example analysis shows that the model can effectively improve the flexibility of the system, reduce carbon emission, maintain the safety of the system under various uncertain conditions and provide a low-carbon, economic and safe scheduling scheme.

Description

Scheduling method of park integrated energy system considering price-based demand response and V2G

technical field

The invention belongs to the technical field of integrated energy system optimization operation, and in particular relates to a park integrated energy system scheduling method considering price-based demand response and V2G.

Background technique

In recent years, with the intensification of the global energy crisis and environmental problems, it has become the consensus of all countries to develop clean energy and improve energy quality. The power sector in countries around the world is transitioning to a sustainable energy system, and the penetration of renewable energy sources such as wind and solar is increasing. The integrated energy system (IES) of the park is the most intuitive form of the energy Internet. It couples multiple energy systems, improves energy utilization, and reduces the operating costs of energy systems. IES is expected to be the key to energy development. However, with the increasing popularity of renewable energy, new challenges have emerged in the supply and demand balance of IES, and the uncertainty of renewable energy power generation needs to be resolved urgently. In this context, it is of great significance to study the low-carbon robust economic dispatching problem of park integrated energy system considering uncertainty.

Uncertainty affects capacity allocation, system cost, and operational characteristics for IES planning and scheduling. Most scholars use stochastic optimization to deal with the uncertainty of IES, but this solution cannot guarantee the safe operation of the system in the worst case. Although methods such as interval optimization, fuzzy optimization, and hybrid optimization have been proposed continuously, the work on the two-stage robust day-ahead scheduling of park integrated energy systems is still quite limited. At the same time, with the continuous maturity of demand response technology, demand response has gradually become an effective means to improve the operating efficiency of IES, and price-based combined heat and power demand response and V2G (Vehicle-to-grid vehicles to grid) participate in the low-carbon economic dispatch of IES It's worth digging into. In addition, the low-carbon economic operation of the park's comprehensive energy system requires the joint action of various low-carbon technologies and a reasonable market mechanism. However, at present, carbon emissions are mostly reduced by optimizing the coordinated operation of carbon capture systems, cogeneration units, and power-to-gas equipment. There is no further study of the carbon trading mechanism or the impact of the entire carbon utilization cycle on carbon emissions.

Therefore, on the basis of the comprehensive energy system composition of the park including cogeneration units, gas units, electric boilers, and power-to-gas coupling equipment, price-based combined heat and power demand response and V2G technology are introduced to further study the carbon trading mechanism and the entire carbon utilization cycle. The impact on carbon emissions and the uncertainty of wind power output and load are of great significance to the low-carbon robust economic dispatch of the integrated energy system in the park.

Contents of the invention

The technical problem to be solved by the present invention is to provide a park integrated energy system dispatching method considering price-based demand response and V2G, which improves the energy utilization rate and security of the park’s basic scene by using price-based combined heat and power demand response and V2G technology . Through the coordinated operation of carbon capture equipment, carbon storage equipment and power-to-gas equipment, a carbon utilization cycle is formed in the system to reduce system carbon emissions. The low-carbon robust economic dispatch model of the whole park integrated energy system can not only promote renewable energy power generation and low-carbon operation, but also maintain system security and carbon emissions under worst-case conditions.

In order to solve the problems of the technologies described above, the technical solution adopted in the present invention is:

A park integrated energy system scheduling method considering price-based demand response and V2G, including the following steps:

Step 1: Model the price-based combined heat and power demand response, V2G and carbon trading respectively, taking into account the park energy balance constraints, operation constraints, heat storage/gas storage constraints and power exchange constraints with the main grid, in order to maximize social welfare as The goal is to build a deterministic model of low-carbon economic scheduling of the park's comprehensive energy system considering price-based combined heat and power demand response and V2G;

Step 2: Through cogeneration units, gas turbines, carbon capture equipment, carbon storage equipment, and power-to-gas equipment, a complete park comprehensive energy system carbon utilization cycle that considers carbon emissions, carbon capture, carbon storage, carbon trading, and carbon consumption is formed. , and model the carbon flow;

Step 3: Based on the deterministic model of low-carbon economic dispatch of park integrated energy system considering the price-based combined heat and power demand response and V2G, introduce two-stage robust optimization to deal with the park's wind power output and electricity/heat load uncertainty, and construct a consideration A low-carbon robust economic dispatch model for the integrated energy system of parks based on price-based combined heat and power demand response and V2G;

Step 4: Transform the double-layer maximum-minimum sub-problem of the low-carbon robust economic dispatch model of the park integrated energy system considering price-type combined heat and power demand response and V2G into a single-layer maximal problem, and use the extreme point method to solve the single-layer problem The bilinear optimization problem within the extremely large problem, and finally use the column sum constraint generation method to solve the low-carbon robust economic dispatch model of the park integrated energy system considering the price-type joint heat and power demand response and V2G;

Step 5: Input the park comprehensive energy system data, equipment parameters, and operating parameters, and use the commercial solver GUROBI to solve the low-carbon robust economic dispatch model of the park comprehensive energy system considering price-type joint heat and power demand response and V2G, and obtain the park comprehensive energy System low-carbon economy robust scheduling optimization results.

Further, the deterministic model of low-carbon economic scheduling of the integrated energy system of the park considering the price-based combined heat and power demand response and V2G in step 1 is as follows:

(1) Objective function:

为二氧化碳相关成本；C^o为园区运行成本；C^cur为弃风/光惩罚成本；C^loss为失负荷惩罚成本；t为调度时间；e表示电负荷；h表示热负荷；k为分段数；

和

分别表示第k段的需求响应电负荷e和热负荷h在t时刻的投标价格；P_ekt表示第k段的需求响应电负荷e在t时刻的电功率，H_hkt表示第k段的需求响应热负荷h在t时刻的热功率；C^tran为碳交易价格；D_t表示园区t时刻的碳排放配额；

表示园区t时刻的碳排放量；C^buy表示向碳市场购碳的单位价格；C^sell表示向碳市场售碳的单位价格；

表示t时刻购碳量；

表示t时刻售碳量；

和

分别表示园区向上级电网购电价格和售电价格；

和

分别表示园区向上级电网购电率和售电功率；

为购气价；

为园区向上级气网购气功率；r和w分别为风机和光伏的索引；

和

分别表示弃风和弃光惩罚单位价格；

和

分别表示园区t时刻的弃风功率和弃光功率；

和

分别表示失电负荷的惩罚价格和失热负荷的惩罚价格；v_et和v_ht分别表示失电负荷变量和失热负荷变量；In the formula: C ^dr is the income obtained by the price type combined heat and power demand response;

C ^o is the operating cost of the park; C ^cur is the wind/light penalty cost; C ^loss is the load loss penalty cost; t is the scheduling time; e is the electric load; h is the heat load; k is the number of segments ;

and

Respectively represent the bidding price of demand response electric load e and heat load h of segment k at time t; P _{ekt represents the electric power of demand response electric load e of segment k at time t, H hkt represents} the demand response heat of segment k The thermal power of load h at time t; C ^tran is the carbon trading price; D _t is the carbon emission quota of the park at time t;

Indicates the carbon emissions of the park at time t; C ^buy indicates the unit price of carbon purchased from the carbon market; C ^sell indicates the unit price of carbon sold to the carbon market;

Indicates the amount of carbon purchased at time t;

Indicates the amount of carbon sold at time t;

and

Respectively represent the park's electricity purchase price and electricity sales price from the upper grid;

and

Respectively represent the power purchase rate and power sales power from the park to the upper grid;

is the gas purchase price;

Purchase gas power from the superior gas network for the park; r and w are the indexes of wind turbine and photovoltaic respectively;

and

Respectively represent the unit price of wind curtailment and solar curtailment penalty;

and

Respectively represent the curtailed wind power and curtailed optical power of the park at time t;

and

Respectively represent the penalty price of power loss load and the penalty price of heat loss load; v _et and v _ht represent the variables of power loss load and heat loss load respectively;

(2) Constraints:

(2.1) Price-type combined heat and power demand response constraints

When the bidding price of demand response is less than the time-of-use electricity price, the price-type responsive load participates in the operation scheduling of the park; when the responsive load is positive, it means that the electric load is cut or transferred to other operating time, and when the responsive load is negative, it means this time Get the load shifted from other moments while increasing:

和

分别表示电负荷转入时间和转出时间；

和

分别表示电负荷最小转入时间和转出时间；Y_et和Y_e,t-1分别表示t时刻和t-1时刻电负荷转移状态的0-1变量，转出为1，转入为0；P_et表示园区实际电负荷功率；

表示预测电负荷功率；P_ekt表示电负荷在第k段t时刻的电功率；

表示可响应电负荷；

表示第k段最大电功率；M为足够大的正数；α_et表示可响应电负荷比例；；

表示t时刻最大电负荷功率；

表示电负荷整体消减量；

和

分别表示热负荷转入时间和转出时间；

和

分别表示热负荷最小转入时间和转出时间；Y_ht和Y_h,t-1分别表示t时刻和t-1时刻热负荷转移状态的0-1变量，转出为1，转入为0；H_ht表示园区实际热负荷功率；；

表示预测热负荷功率；H_hkt表示热负荷在第k段t时刻的热功率；

表示可响应热负荷；

表示第k段最大热功率；α_ht表示可响应热负荷比例；

表示t时刻最大热负荷功率；

表示热负荷整体消减量；In the formula:

and

Respectively represent the transfer-in time and transfer-out time of electric load;

and

Indicate the minimum transfer-in time and transfer-out time of the electric load respectively; Y _et and Y _e,t-1 represent the 0-1 variables of the electric load transfer state at time t and t-1 respectively, the transfer-out is 1, and the transfer-in is 0 ; P _et represents the actual electric load power of the park;

Indicates the predicted electric load power; P _ekt represents the electric power of the electric load at the kth segment t time;

Indicates that it can respond to electrical loads;

Indicates the maximum electric power of the kth section; M is a sufficiently large positive number; _αet represents the proportion of the electric load that can be responded to;;

Indicates the maximum electric load power at time t;

Indicates the overall reduction of electric load;

and

Respectively represent the heat load transfer-in time and transfer-out time;

and

respectively represent the minimum heat load transfer-in time and transfer-out time; Y _ht and Y _h,t-1 respectively represent the 0-1 variable of the heat load transfer state at time t and t-1, the transfer-out is 1, and the transfer-in is 0 ; H _ht represents the actual heat load power of the park;

Indicates the predicted thermal load power; H _hkt indicates the thermal power of the thermal load at the kth segment t time;

Indicates that it can respond to thermal load;

Indicates the maximum thermal power of section k; α _ht indicates the proportion of responsive thermal load;

Indicates the maximum thermal load power at time t;

Indicates the overall reduction of heat load;

(2.2) V2G constraints

表示电动汽车充电状态，充电为1，否则为0；

表示电动汽车放电状态，放电为1，否则为0；

为接入时刻和充放电时间之和；

分别表示电动汽车充、放电功率；

分别表示电动汽车额定充电效率和放电功率；

表示电动汽车接入时刻的0-1变量，接入时刻为1，其余时刻为0；M表示足够大正数；

表示电动汽车电池荷电状态；

表示电动汽车初始荷电状态；

表示电动汽车t-1时刻的电池荷电状态；

和

分别表示电动汽车充电效率和放电效率；

表示电动汽车电池容量；

表示电动汽车离开时刻，离开时刻为1，其余时刻为0；

表示电动汽车离开时刻电池荷电状态；

和

分别表示电池荷电状态的下限和上限；In the formula: l is the index of the electric vehicle;

Indicates the charging state of the electric vehicle, 1 for charging, 0 otherwise;

Indicates the discharge state of the electric vehicle, discharge is 1, otherwise it is 0;

is the sum of access time and charging and discharging time;

Respectively represent the charging and discharging power of electric vehicles;

respectively represent the rated charging efficiency and discharging power of electric vehicles;

Indicates the 0-1 variable of the access time of the electric vehicle, the access time is 1, and the rest of the time is 0; M indicates a sufficiently large positive number;

Indicates the state of charge of the electric vehicle battery;

Indicates the initial state of charge of the electric vehicle;

Indicates the state of charge of the battery at time t-1 of the electric vehicle;

and

Respectively represent the charging efficiency and discharging efficiency of electric vehicles;

Indicates the battery capacity of electric vehicles;

Indicates the departure time of the electric vehicle, the departure time is 1, and the rest of the time is 0;

Indicates the state of charge of the battery when the electric vehicle leaves;

and

Respectively represent the lower limit and upper limit of the battery state of charge;

(2.3) Carbon capture and storage constraints

表示园区t时刻的碳排放量；p、q分别为CHP和燃气轮机的索引；

表示第p台CHP在t时刻的碳排放量；

表示第q台燃气轮机在t时刻的碳排放量；

表示从上级电网购电产生的碳排放量；i为碳捕集机组的索引；

表示第i台碳捕集机组捕集的二氧化碳量；

和

分别为储碳设备的碳存入量和输出量；

表示园区t时刻的购碳量；m为P2G的索引；

表示第m台P2G在t时刻的碳消耗量；

表示园区t时刻的售碳量；

为碳捕集率；

和μ^upper分别表示CHP、燃气轮机和主网的碳排放强度；

和

分别表示CHP和燃气轮机在t时刻的出力；

表示生成单位功率天然气需要的二氧化碳量；

表示第m台P2G的电转气效率；

表示第m台P2G在t时刻的耗电功率；L^HANG表示天然气低热值；

表示储碳设备储碳量；

表示储碳设备在t-1时刻的储碳量；η^s为储碳损耗系数；C^s,min和C^s,max分别表示储碳设备的最小储碳量和最大储碳量；M^in,min和M^in,max表示储碳设备最小碳存入量和最大碳存入量；M^out,min和M^out,max为储碳设备最小碳输出量和最大碳输出量；M^b,max表示园区外购碳量的最大值；M^s,max表示园区售碳量的最大值；

表示第i台碳捕集机组t时刻的耗电功率；θ为处理单位二氧化碳的能耗；

表示碳捕集设备启停状态，开机为1，关机为0；

表示碳捕集设备的固定能耗；In the formula:

Indicates the carbon emissions of the park at time t; p and q are the indexes of CHP and gas turbine respectively;

Indicates the carbon emission of the pth CHP at time t;

Indicates the carbon emission of the qth gas turbine at time t;

Indicates the carbon emissions generated by purchasing electricity from the upper-level grid; i is the index of the carbon capture unit;

Indicates the amount of carbon dioxide captured by the i-th carbon capture unit;

and

are the carbon storage and output of carbon storage equipment, respectively;

Indicates the amount of carbon purchased in the park at time t; m is the index of P2G;

Indicates the carbon consumption of the mth P2G at time t;

Indicates the amount of carbon sold in the park at time t;

is the carbon capture rate;

and μ ^upper represent the carbon emission intensity of CHP, gas turbine and main network respectively;

and

respectively represent the output of CHP and gas turbine at time t;

Indicates the amount of carbon dioxide required to generate unit power of natural gas;

Indicates the power-to-gas efficiency of the mth P2G;

Indicates the power consumption of the mth P2G at time t; L ^HANG indicates the low calorific value of natural gas;

Indicates the carbon storage capacity of the carbon storage equipment;

Indicates the carbon storage capacity of the carbon storage equipment at time t-1; η ^s is the carbon storage loss coefficient; C ^s,min and C ^s,max respectively represent the minimum carbon storage capacity and maximum carbon storage capacity of the carbon storage equipment; Min ^{, min} and M ^in,max represent the minimum carbon storage and maximum carbon storage of carbon storage equipment; M ^out,min and M ^out,max are the minimum carbon output and maximum carbon output of carbon storage equipment; M ^b,max represent The maximum amount of purchased carbon in the park; M ^s,max represents the maximum amount of carbon sold in the park;

Indicates the power consumption of the i-th carbon capture unit at time t; θ is the energy consumption per unit of carbon dioxide;

Indicates the start-stop status of the carbon capture equipment, 1 for power-on and 0 for power-off;

Indicates the fixed energy consumption of carbon capture equipment;

(2.4) Energy balance constraints

和

分别表示第r台风机和第w台光伏t时刻的出力；P_et为t时刻考虑需求响应后的实际电负荷量；n为电锅炉的索引；

表示第n台电锅炉在t时刻的耗电功率；

为第m台P2G在t时刻产生的气功率；

和

分别为储气设备t时刻储存和释放的气功率；

和

分别为CHP和燃气轮机消耗的气功率；η^heat为园区的热能利用率；

和

分别为CHP和电锅炉的产热功率；

和

分别为储热设备储存和释放的热功率。In the formula:

and

Respectively represent the output of the rth wind turbine and the wth photovoltaic unit at time t; P _et is the actual electric load after considering demand response at time t; n is the index of the electric boiler;

Indicates the power consumption of the nth electric boiler at time t;

is the gas power generated by the m-th P2G at time t;

and

are the gas power stored and released by the gas storage equipment at time t, respectively;

and

are the gas power consumed by CHP and gas turbine respectively; η ^heat is the thermal energy utilization rate of the park;

and

are the heat production power of CHP and electric boiler, respectively;

and

are the thermal power stored and released by the heat storage device, respectively.

(2.5) Power exchange constraints with the main network

In the formula: P ^in,min and P ^in,max respectively represent the minimum and maximum electric power purchased from the main network; P ^out,min and P ^out,max are the minimum and maximum electric power sold to the main network; G ^in,min and G ^in,max are the minimum and maximum gas power purchased from the main network, respectively;

(2.6) Abandoned wind and light constraints and lost load constraints

和

分别为允许的弃风比例、弃光比例、失电负荷比例和失热负荷比例；In the formula:

and

Respectively, the allowable wind curtailment ratio, solar curtailment ratio, power loss load ratio and heat loss load ratio;

(2.7) Operational constraints

(2.7.1) CHP operation constraints

和

分别为CHP内溴冷机的制热系数和烟气回收率；

为CHP内微燃机的发电效率；

为散热损失率；

和

分别为CHP的开机成本和关机成本；

和

分别为CHP单次开机成本和关机成本；

和

分别为CHP在t时刻和t-1时刻的开关机状态，开机为1，关机为0；

和

分别为CHP出力的最小电功率和最大电功率；

为CHP在t-1时刻的出力；

和

分别为CHP的上爬坡率和下爬坡率；

分别为CHP的连续开机和关机时间；

分别为CHP的最小开机时间和最小关机时间；In the formula:

and

Respectively, the heating coefficient and flue gas recovery rate of the CHP internal bromine refrigerator;

is the power generation efficiency of the CHP internal micro-combustion engine;

is the heat loss rate;

and

are the start-up cost and shutdown cost of CHP, respectively;

and

Respectively, CHP single start-up cost and shutdown cost;

and

Respectively, the switching status of CHP at time t and time t-1, 1 for power-on and 0 for power-off;

and

Respectively, the minimum electric power and maximum electric power of CHP output;

is the contribution of CHP at time t-1;

and

Respectively, the up-slope rate and the down-slope rate of CHP;

Respectively, the continuous power-on and power-off time of CHP;

Respectively, the minimum power-on time and the minimum power-off time of CHP;

(2.7.2) Gas turbine operating constraints

分别为CHP的开机成本和关机成本；

为燃气轮机出力最小值；

为燃气轮机在t时刻的开关机状态，开机为1，关机为0；

为燃气轮机在第k段的耗气增量；

为燃气轮机在t时刻第k段的电功率；In the formula: F( ) is the heat rate curve of the gas turbine;

are the start-up cost and shutdown cost of CHP, respectively;

It is the minimum output value of the gas turbine;

is the on/off state of the gas turbine at time t, 1 for power on and 0 for power off;

is the gas consumption increment of the gas turbine in the k-th section;

is the electric power of the gas turbine at the kth segment at time t;

(2.7.3) P2G operation constraints

为P2G的电转气效率；

和

分别为P2G的最小制气功率和最大制气功率；In the formula:

is the power-to-gas efficiency of P2G;

and

Respectively, the minimum gas production power and maximum gas production power of P2G;

(2.7.4) Electric Boiler Operation Constraints

为电锅炉的电制热效率；

分别为电锅炉的最小制热功率和最大制热功率；In the formula:

is the electric heating efficiency of the electric boiler;

Respectively, the minimum heating power and maximum heating power of the electric boiler;

(2.7.5) Operation constraints of gas storage and heat storage equipment

和

分别为储气设备的储气功率和放气功率；G^GS,in,max和G^GS，out,max分别为储气设备的最大储气功率和最大放气功率；

和

分别为储气设备在t时刻和t-1时刻的储气容量；η^CGS、η^GS,in和η^GS,out分别为储气设备自耗率、储气效率和放气效率；

和

分别为储热设备的储热功率和放热功率；H^HS,in,max和H^HS,out,max分别为储热设备的最大储热功率和最大放热功率；

和

分别为储热设备在t时刻和t-1时刻的储热容量；η^CHS、η^HS,in和η^HS,out分别为储热设备自耗率、储热效率和放热效率；In the formula:

and

are the gas storage power and gas discharge power of the gas storage equipment; G ^GS,in,max and G ^GS,out,max are the maximum gas storage power and maximum gas discharge power of the gas storage equipment;

and

are the gas storage capacity of the gas storage equipment at time t and t-1 respectively; η ^CGS , η ^GS,in and η ^GS,out are the self-consumption rate, gas storage efficiency and gas release efficiency of the gas storage equipment, respectively;

and

are the heat storage power and heat release power of the heat storage equipment, respectively; H ^HS,in,max and H ^HS,out,max are the maximum heat storage power and maximum heat release power of the heat storage equipment, respectively;

and

are the heat storage capacity of the heat storage equipment at time t and t-1, respectively; η ^CHS , η ^HS,in and η ^HS,out are the self-consumption rate, heat storage efficiency, and heat release efficiency of the heat storage equipment, respectively;

(2.8) General vector form

Write the above deterministic optimal scheduling model in general vector form:

s.t.Ax+By+Cv≤b,x∈{0,1}

和

是目标函数的常系数向量；A、B、C和b分别为约束常系数矩阵和向量。In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

and

is the constant coefficient vector of the objective function; A, B, C and b are constrained constant coefficient matrix and vector, respectively.

Further, the carbon utilization cycle of the complete park integrated energy system considering carbon emission, carbon capture, carbon storage, carbon trading and carbon consumption mentioned in step 2 is as follows:

Carbon capture equipment captures carbon dioxide produced during the operation of cogeneration units and gas turbines, and supplies the captured carbon dioxide directly to power-to-gas equipment to generate natural gas. The excess carbon dioxide is stored in carbon storage equipment or directly traded with external carbon markets or direct discharge.

Furthermore, the low-carbon robust economic dispatch model of the park integrated energy system considering the price-based combined heat and power demand response and V2G in step 3 is as follows:

Based on the deterministic model of low-carbon economic scheduling of park integrated energy system considering price-based demand response and V2G, the two-stage robust scheduling model considering the uncertainty of wind power output and load forecasting is shown in the following formula; the first part of the model The first stage is the optimal dispatching scheme for decision-making states such as the optimal dispatching of the park’s comprehensive energy system, the charging and discharging state of electric vehicles, and the transition state of price-based demand response under the basic scenario. The second stage is based on the dispatching plan of the first stage, according to the Output fluctuations and load real-time values adjust park unit output, V2G, and demand response loads to ensure safe operation of the system; among them, the maximum and minimum sub-problems are used to identify the worst scenario that may lead to the maximum safety limit of the park under uncertain conditions;

s.t.Ax+By≤b,x∈{0,1}

是目标函数的常系数向量；u为与风电、光伏出力不确定性和负荷值相关的不确定变量；F(x,y)为x与y相关的函数；ε^RO表示允许的安全阈值；A、B、C、D、E、F、G、f、b和g分别为约束常系数矩阵和向量。In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

is the constant coefficient vector of the objective function; u is the uncertain variable related to wind power, photovoltaic output uncertainty and load value; F(x,y) is the function related to x and y; ε ^RO represents the allowable safety threshold; A , B, C, D, E, F, G, f, b and g are constrained constant coefficient matrix and vector, respectively.

Further, the process of solving the low-carbon robust economic dispatch model of the park integrated energy system considering the price-type combined heat and power demand response and V2G by using the dual transformation, extreme point method and CCG method described in step 4 is as follows:

(1) The main problem of robust scheduling of park integrated energy system:

由第s次迭代中的子问题中求解得到，S为迭代的总次数；The objective function of the main problem of robust scheduling is to maximize the social welfare of the park, and the constraints include basic scenario constraints and worst scenario constraints; the worst scenario corresponds to wind power output, photovoltaic output and actual load values

It is obtained by solving the subproblems in the sth iteration, and S is the total number of iterations;

Ax+By≤b,x∈{0,1}

分别为失负荷量、系统连续变量和不确定性变量的第s次迭代值。In the formula, v _s , z _s and

are respectively the values of the lost load, the system continuous variables and the uncertain variables in the sth iteration.

(2) The worst scenario identification sub-problem of the integrated energy system of the park:

The double-layer maximum-minimum sub-problem is the problem of identifying the worst scenario, finding the scenario that causes the maximum violation of the safety regulation value of the system, that is, determining the specific value of the uncertain quantity in the worst scenario; among them, x ^* and y ^* are determined by the main problem Obtained, λ is the dual variable of the linear inequality constraint;

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ)

(3) Transform the double-layer maximum-minimum subproblem into a single-layer maximization problem using dual transformation:

stλ ^T E ≤ f

λ ^T F ≤ 0

λ≤0

(4) Using the extreme point method to solve the bilinear variable product λu problem in the single-layer maximization problem:

λ＝λ ⁰ +λ ⁺ +λ ^-

β ⁰ +β ⁺ +β ^- ＝1

-β ⁰ M≤λ ⁰ ≤β ⁰ M

-β ⁺ M≤λ ⁺ ≤β ⁺ M

-β ^- M≤λ ^- ≤β ^- M

In the formula: λ ⁰ , λ ⁺ and λ ^- are auxiliary continuous variables, β ⁰ , β ⁺ and β ^- are auxiliary 0-1 variables, corresponding to u take the upper limit u ⁺ , mean u ^b , and lower limit u ^- of the uncertain set situation; M is a very large number;

(5) The specific process of CCG method to solve the proposed low-carbon robust economic dispatch model of park integrated energy system considering price-based demand response and V2G:

Step a: Let the iteration counter s=0, and set the maximum value ε ^RO that the system allows to violate safety regulations;

Step b: Solve the main problem. If there is a solution, obtain the decision state x such as the start-stop state of the system unit and the unit output arrangement y, and proceed to step c; otherwise, stop the iteration and output no solution;

Step c: According to the x and y obtained in step b, solve the maximum and minimum sub-problems, and find the wind power output and load value in the worst scenario that may cause the maximum possible violation of safety regulations;

向主问题中增加如下式所示的CCG约束，返回步骤b；Step d: If the maximum possible safety violation value obtained in step c is less than ε ^RO , then x and y are the final optimization scheme and the iteration is stopped; otherwise, let s=s+1, according to the worst value obtained in step c Wind power, photovoltaic output value and load value in the scenario

Add the CCG constraint shown in the following formula to the main problem, and return to step b;

f ^T v _s ≤ε ^RO

In the formula, v _s and z _s are respectively the loss of load and the sth iteration value of the continuous variable of the system.

Furthermore, the park comprehensive energy system data described in step 5 also includes the specific composition of the park comprehensive energy system and the topology of electric-gas-thermal energy flow, and the park comprehensive energy system equipment parameters include fans, photovoltaic cells, cogeneration units, Gas turbines, electric boilers, P2G, gas storage equipment, heat storage equipment, electric vehicles, carbon capture equipment, and carbon storage equipment, the quantity, capacity, output/upper and lower limits of charge and discharge power, the operating parameters of the comprehensive energy system in the park include Grid electricity purchase price, gas purchase price from higher-level gas network, carbon transaction price and various operating parameters of the above-mentioned equipment, price-based combined heat and power demand response ratio, and electric heating load forecast data.

Compared with prior art, the beneficial effect of the present invention is:

1) Considering V2G technology and price-based joint heat and power demand response, a two-stage robust dispatching model of the park's integrated energy system is established. By adaptively adjusting the charging/discharging of electric vehicles and shifting the electric/thermal loads from peak hours to off-peak hours through price-type joint heat and power demand response, the proposed robust model can improve the system operating efficiency in the base case, while at the same time Ensuring system security in the presence of uncertainty.

2) The carbon flow of the integrated energy system of the park is modeled in detail, which considers carbon emission, carbon capture, carbon storage, carbon trading, and carbon consumption through various equipment, forming a complete carbon utilization cycle. In addition, considering the carbon trading mechanism where exceeding the carbon emission quota may be subject to huge penalties, the two-stage robust dispatch model can also keep the carbon emission of the park within an acceptable range when the wind and photovoltaic power generation is low.

3) V2G technology can effectively prevent electric vehicles from charging during peak hours, thereby reducing the peak-to-valley difference, alleviating system operating pressure, and reducing the operating cost of the park's comprehensive energy system; price-based combined heat and power demand response can enhance system flexibility and significantly reduce The cost of electricity purchase promotes the penetration rate of renewable energy; carbon emission trading guides the system to actively adopt clean production methods to maintain load balance. Through the sensitivity analysis of carbon emissions trading, it is proved that a reasonable pricing mechanism can significantly reduce carbon emissions.

Description of drawings

Fig. 1 is a flowchart of the steps of the method of the present invention.

Figure 2 is a diagram of the demand response ladder price curve visually showing the change of the price response load relative to the change of the electricity price.

Figure 3 is a schematic diagram of the carbon utilization cycle of the park's comprehensive energy system.

Figure 4 is a specific composition diagram of the park's comprehensive energy system.

Figure 5 is the net electricity load diagram under the economic dispatch of the integrated energy system of the park without considering the carbon trading mechanism.

Figure 6 is the heat load diagram under the economic dispatch of the integrated energy system of the park without considering the carbon trading mechanism.

Figure 7 shows the changes in purchased power, combined heat and power unit output, gas turbine output, and sold power under the low-carbon robust economic dispatch of the integrated energy system of the park considering different carbon transaction prices.

detailed description

In order to describe the technical solutions disclosed in the present invention in detail, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

The invention discloses a low-carbon robust economic scheduling operation method for a park comprehensive energy system considering price-based demand response and V2G. As shown in Figure 1, the specific implementation steps process, the technical solution of the present invention comprises the following steps:

Step 1: Model the price-based combined heat and power demand response, V2G and carbon trading respectively, taking into account the park energy balance constraints, operation constraints, heat storage/gas storage constraints and power exchange constraints with the main grid, in order to maximize social welfare as Objective To construct a deterministic model of low-carbon economic dispatch of park integrated energy system considering price-based combined heat and power demand response and V2G.

(1.1) Objective function:

为二氧化碳相关成本；C^o为园区运行成本；C^cur为弃风/光惩罚成本；C^loss为失负荷惩罚成本；t为调度时间；e表示电负荷；h表示热负荷；k为分段数；

和

分别表示第k段的需求响应电负荷e/热负荷h在t时刻的投标价格；P_ekt和H_hkt分别表示第k段的需求响应电/热负荷在t时刻的电/热功率；C^tran为碳交易价格；D_t表示园区t时刻的碳排放配额；

表示园区t时刻的碳排放量；C^buy表示向碳市场购碳的单位价格；C^sell表示向碳市场售碳的单位价格；

表示t时刻购碳量；

表示t时刻售碳量；

和

分别表示园区向上级电网购电、售电价格；

和

分别表示园区向上级电网购电、售电功率；

为购气价；

为园区向上级气网购气、售气功率；r、w分别为风机和光伏的索引；

分别表示弃风和弃光惩罚单位价格；

分别表示园区t时刻的弃风功率和弃光功率；

和

分别表示失电/热负荷的惩罚价格；v_et和v_ht分别表示失电负荷/热负荷变量。In the formula: C ^dr is the income obtained by the price type combined heat and power demand response;

C ^o is the operating cost of the park; C ^cur is the wind/light penalty cost; C ^loss is the load loss penalty cost; t is the scheduling time; e is the electric load; h is the heat load; k is the number of segments ;

and

Respectively represent the bidding price of demand response electric load e/thermal load h in segment k at time t; P _{ekt and H hkt respectively} represent the electric/thermal power of demand response electric/thermal load in segment k at time t ^; is the carbon trading price; D _t represents the carbon emission quota of the park at time t;

Indicates the carbon emissions of the park at time t; C ^buy indicates the unit price of carbon purchased from the carbon market; C ^sell indicates the unit price of carbon sold to the carbon market;

Indicates the amount of carbon purchased at time t;

Indicates the amount of carbon sold at time t;

and

Respectively represent the price of electricity purchase and sale from the park to the upper grid;

and

Respectively represent the power purchased and sold by the park from the upper grid;

is the gas purchase price;

Purchase gas and sell gas power for the park to the upper gas network; r and w are the indexes of wind turbines and photovoltaics respectively;

Respectively represent the unit price of wind curtailment and solar curtailment penalty;

Respectively represent the curtailed wind power and curtailed optical power of the park at time t;

and

respectively represent the penalty price of electricity loss/heat load; v _et and v _ht represent the variables of electricity loss/heat load respectively.

(1.2) Constraints:

(1.2.1) Price-type combined heat and power demand response constraints

The energy consumption of price-responsive loads will decrease monotonically with the increase of electricity prices, and the demand-response ladder price curve shown in Figure 2 can be used to intuitively represent the changes of price-responsive loads relative to changes in electricity prices. According to changes in energy market prices, price-responsive loads can be cut or transferred to other operating time periods, that is, when demand response bidding prices are lower than time-of-use electricity prices, price-type responsive loads participate in park operation scheduling. Responsive loads are positive When the value is negative, it means that the electric load is reduced or transferred to other operating time, and when the responsive load is negative, it means that this time gets the load transferred from other time and increases.

和

分别表示电负荷转入/转出时间；

和

分别表示电负荷最小转入/转出时间；Y_et表示电负荷转移状态的0-1变量，转出为1；P_et表示园区实际电负荷功率；α_et表示可响应电负荷比例；

表示预测电负荷功率；P_ekt表示电负荷在第k段t时刻的电功率；

表示可响应电负荷；

表示第k段最大电功率；M为足够大的正数；

表示t时刻最大电负荷功率；

表示电负荷整体消减量；

和

分别表示热负荷转入/转出时间；

和

分别表示热负荷最小转入/转出时间；Y_ht表示热负荷转移状态的0-1变量，转出为1；H_ht表示园区实际热负荷功率；α_ht表示可响应热负荷比例；

表示预测热负荷功率；H_hkt表示热负荷在第k段t时刻的热功率；

表示可响应热负荷；

表示第k段最大热功率；

表示t时刻最大热负荷功率；

表示热负荷整体消减量。In the formula:

and

Respectively represent the transfer-in/transfer-out time of electric load;

and

Indicates the minimum transfer-in/out time of electric load respectively; Y _et represents the 0-1 variable of electric load transfer state, and transfer-out is 1; P _et represents the actual electric load power of the park; α _et represents the proportion of electric load that can be responded;

Indicates the predicted electric load power; P _ekt represents the electric power of the electric load at the kth segment t time;

Indicates that it can respond to electrical loads;

Indicates the maximum electric power of the kth section; M is a positive number that is large enough;

Indicates the maximum electric load power at time t;

Indicates the overall reduction of electric load;

and

Respectively represent the heat load transfer-in/transfer-out time;

and

Respectively represent the minimum heat load transfer-in/transfer-out time; Y _ht represents the 0-1 variable of the heat load transfer state, and the transfer out is 1; H _ht represents the actual heat load power of the park; α _ht represents the proportion of the heat load that can be responded;

Indicates the predicted thermal load power; H _hkt indicates the thermal power of the thermal load at the kth segment t time;

Indicates that it can respond to thermal load;

Indicates the maximum thermal power of the kth section;

Indicates the maximum thermal load power at time t;

Indicates the overall reduction of heat load.

(1.2.2) V2G constraints

表示电动汽车接入时刻的0-1变量，接入时刻为1，其余时刻为0；

为接入时刻和充/放电时间之和；

分别表示电动汽车充、放电功率；

表示电动汽车充电状态，充电为1，否则为0；

表示电动汽车放电状态，放电为1，否则为0；

分别代表电动汽车额定充/放电功率；M表示足够大正数；

表示电动汽车电池荷电状态；

表示电动汽车初始荷电状态；

表示电动汽车t-1时刻的电池荷电状态；

和

分别表示电动汽车充/放电效率；

表示电动汽车电池容量；

表示电动汽车离开时刻，离开时刻为1，其余时刻为0；

表示电动汽车离开时刻电池荷电状态；

和

分别表示电池荷电状态的下限和上限。In the formula: l is the index of the electric vehicle;

The 0-1 variable representing the access time of the electric vehicle, the access time is 1, and the rest of the time is 0;

is the sum of access time and charging/discharging time;

Respectively represent the charging and discharging power of electric vehicles;

Indicates the charging state of the electric vehicle, 1 for charging, 0 otherwise;

Indicates the discharge state of the electric vehicle, discharge is 1, otherwise it is 0;

Respectively represent the rated charge/discharge power of electric vehicles; M represents a sufficiently large positive number;

Indicates the state of charge of the electric vehicle battery;

Indicates the initial state of charge of the electric vehicle;

Indicates the state of charge of the battery at time t-1 of the electric vehicle;

and

Respectively represent the charging/discharging efficiency of electric vehicles;

Indicates the battery capacity of electric vehicles;

Indicates the departure time of the electric vehicle, the departure time is 1, and the rest of the time is 0;

Indicates the state of charge of the battery when the electric vehicle leaves;

and

Represents the lower and upper limits of the battery state of charge, respectively.

(1.2.3) Carbon capture and storage constraints

表示第p台CHP在t时刻的碳排放量；

表示第q台燃气轮机在t时刻的碳排放量；

表示从上级电网购电产生的碳排放量；i为碳捕集机组的索引；

表示第i台碳捕集机组捕集的二氧化碳量；

和

分别为储碳设备的碳存入/输出量；m为P2G的索引；

表示第m台P2G在t时刻的碳消耗量；

为碳捕集率；

和μ^upper分别表示CHP、燃气轮机和主网的碳排放强度；

分别表示CHP和燃气轮机在t时刻的出力；

表示生成单位功率天然气需要的二氧化碳量；

表示第m台P2G的电转气效率；

表示第m台P2G在t时刻的耗电功率；L^HANG表示天然气低热值，取值9.7kW·h/m³；

表示储碳设备储碳量；

表示储碳设备在t-1时刻的储碳量；η^s为储碳损耗系数；C^s,min和C^s,max分别表示储碳设备的最小/最大储碳量；M^in,min和M^{in ,max}表示储碳设备最小/最大碳存入量；M^out,min和M^out,max为储碳设备最小/最大碳输出量；M^{b ,max}表示园区外购碳量的最大值；M^s,max表示园区售碳量的最大值；

表示第i台碳捕集机组t时刻的耗电功率；θ为处理单位二氧化碳的能耗；

表示碳捕集设备启停状态，开机为1，关机为0；

表示碳捕集设备的固定能耗。In the formula: p and q are the indexes of CHP and gas turbine respectively;

Indicates the carbon emission of the pth CHP at time t;

Indicates the carbon emission of the qth gas turbine at time t;

Indicates the carbon emissions generated by purchasing electricity from the upper-level grid; i is the index of the carbon capture unit;

Indicates the amount of carbon dioxide captured by the i-th carbon capture unit;

and

are the carbon storage/output of carbon storage equipment; m is the index of P2G;

Indicates the carbon consumption of the mth P2G at time t;

is the carbon capture rate;

and μ ^upper represent the carbon emission intensity of CHP, gas turbine and main network respectively;

respectively represent the output of CHP and gas turbine at time t;

Indicates the amount of carbon dioxide required to generate unit power of natural gas;

Indicates the power-to-gas efficiency of the mth P2G;

Indicates the power consumption of the mth P2G at time t; L ^HANG indicates the low calorific value of natural gas, which is 9.7kW·h/m ³ ;

Indicates the carbon storage capacity of the carbon storage equipment;

Indicates the carbon storage capacity of the carbon storage equipment at time t-1; η ^s is the carbon storage loss coefficient; C ^s,min and C ^s,max respectively indicate the minimum/maximum carbon storage capacity of the carbon storage equipment; Min ^,min and M ^{in , max} indicates the minimum/maximum carbon storage amount of carbon storage equipment; M ^out,min and M ^out,max are the minimum/maximum carbon output of carbon storage equipment; M ^{b ,max} indicates the maximum amount of purchased carbon in the park; M ^s,max represents the maximum value of carbon sales in the park;

Indicates the power consumption of the i-th carbon capture unit at time t; θ is the energy consumption per unit of carbon dioxide;

Indicates the start-stop status of the carbon capture equipment, 1 for power-on and 0 for power-off;

Indicates the fixed energy consumption of carbon capture equipment.

(1.2.4) Energy balance constraints

和

分别表示第r台风机和第w台光伏t时刻的出力；P_et为t时刻考虑需求响应后的实际电负荷量；n为电锅炉的索引；

表示第n台电锅炉在t时刻的耗电功率；

为第m台P2G在t时刻产生的气功率；

分别为储气设备t时刻储存和释放的气功率；

分别为CHP和燃气轮机消耗的气功率；η^heat为园区的热能利用率；

分别为CHP和电锅炉的产热功率；

和

分别为储热设备储存和释放的热功率。In the formula:

and

Respectively represent the output of the rth wind turbine and the wth photovoltaic unit at time t; P _et is the actual electric load after considering demand response at time t; n is the index of the electric boiler;

Indicates the power consumption of the nth electric boiler at time t;

is the gas power generated by the m-th P2G at time t;

are the gas power stored and released by the gas storage equipment at time t, respectively;

are the gas power consumed by CHP and gas turbine respectively; η ^heat is the thermal energy utilization rate of the park;

are the heat production power of CHP and electric boiler, respectively;

and

are the thermal power stored and released by the heat storage device, respectively.

(1.2.5) Power exchange constraints with the main network

In the formula: P ^in,min and P ^in,max respectively represent the minimum and maximum electric power purchased from the main network; P ^out,min and P ^out,max are the minimum and maximum electric power sold to the main network; G ^in,min , G ^in,max are the minimum and maximum gas power purchased from the main network, respectively.

(1.2.6) Constraints of wind and wind abandonment and load loss

和

分别为允许的弃风比例、弃光比例、失电负荷比例和失热负荷比例。In the formula:

and

Respectively, the allowable wind curtailment ratio, solar curtailment ratio, power loss load ratio and heat loss load ratio.

(1.2.7) Operational constraints

(1.2.7.1) CHP operational constraints

和

分别为CHP内溴冷机的制热系数和烟气回收率；

为CHP内微燃机的发电效率；

为散热损失率；

分别为CHP的开机和关机成本；

分别为CHP单次开机和关机的成本；

分别为CHP在t时刻和t-1时刻的开关机状态，开机为1，关机为0；

分别为CHP出力的最小和最大电功率；

为CHP在t-1时刻的出力；

分别为CHP的上爬坡率和下爬坡率；

分别为CHP的连续开机和关机时间；

分别为CHP的最小开机时间和最小关机时间。In the formula:

and

Respectively, the heating coefficient and flue gas recovery rate of the CHP internal bromine refrigerator;

is the power generation efficiency of the CHP internal micro-combustion engine;

is the heat loss rate;

are the startup and shutdown costs of CHP, respectively;

Respectively, the cost of a single startup and shutdown of CHP;

Respectively, the switching status of CHP at time t and time t-1, 1 for power-on and 0 for power-off;

are the minimum and maximum electric power of CHP output respectively;

is the contribution of CHP at time t-1;

Respectively, the up-slope rate and the down-slope rate of CHP;

Respectively, the continuous power-on and power-off time of CHP;

are the minimum power-on time and minimum power-off time of the CHP, respectively.

(1.2.7.2) Gas turbine operating constraints

分别为CHP的开机和关机成本；

为燃气轮机出力最小值；

为燃气轮机在t时刻的开关机状态，开机为1，关机为0；

为燃气轮机在第k段的耗气增量；

为燃气轮机在t时刻第k段的电功率。In the formula: F( ) is the heat rate curve of the gas turbine;

are the startup and shutdown costs of CHP, respectively;

It is the minimum output value of the gas turbine;

is the on/off state of the gas turbine at time t, 1 for power on and 0 for power off;

is the gas consumption increment of the gas turbine in the k-th section;

is the electric power of the gas turbine at the kth stage at time t.

(1.2.7.3) P2G operation constraints

为P2G的电转气效率；

分别为P2G的最小和最大制气功率。In the formula:

is the power-to-gas efficiency of P2G;

are the minimum and maximum gas production power of P2G, respectively.

(1.2.7.4) Electric boiler operating constraints

为电锅炉的电制热效率；

分别为电锅炉的最小和最大制热功率。In the formula:

is the electric heating efficiency of the electric boiler;

are the minimum and maximum heating power of the electric boiler, respectively.

(1.2.7.5) Operation constraints of gas storage and heat storage equipment

分别为储气设备的储/放气功率；G^GS,in,max、G^GS,out,max分别为储气设备的最大储/放气功率；

分别为储气设备在t时刻和t-1时刻的储气容量；η^CGS、η^GS,in和η^GS,out分别为储气设备自耗率、储气效率和放气效率；

分别为储热设备的储/放热功率；H^HS,in,max、H^HS,out,max分别为储热设备的最大储/放热功率；

分别为储热设备在t时刻和t-1时刻的储热容量；η^CHS、η^HS,in和η^HS,out分别为储热设备自耗率、储热效率和放热效率。In the formula:

are the storage/deflation power of the gas storage equipment; G ^GS,in,max and G ^GS,out,max are the maximum storage/deflation power of the gas storage equipment;

are the gas storage capacity of the gas storage equipment at time t and t-1 respectively; η ^CGS , η ^GS,in and η ^GS,out are the self-consumption rate, gas storage efficiency and gas release efficiency of the gas storage equipment, respectively;

are the heat storage/discharge power of the heat storage equipment; H ^HS,in,max and H ^HS,out,max are the maximum heat storage/discharge power of the heat storage equipment;

are the heat storage capacity of the heat storage equipment at time t and t-1, respectively; η ^CHS , η ^HS,in and η ^HS,out are the self-consumption rate, heat storage efficiency, and heat release efficiency of the heat storage equipment, respectively.

(1.2.8) General vector form

For the convenience of discussion, the above deterministic optimal scheduling model is written in general vector form.

s.t.Ax+By+Cv≤b,x∈{0,1}

和

是目标函数的常系数向量；A、B、C和b分别为约束常系数矩阵和向量。In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

and

is the constant coefficient vector of the objective function; A, B, C and b are constrained constant coefficient matrix and vector, respectively.

Step 2: Through cogeneration units, gas turbines, carbon capture equipment, carbon storage equipment, and power-to-gas equipment, etc., form a complete park comprehensive energy system that considers carbon emissions, carbon capture, carbon storage, carbon trading, and carbon consumption. Carbon utilization cycle, and model carbon flows.

The carbon utilization cycle of the park's comprehensive energy system is shown in Figure 3. Carbon capture equipment captures carbon dioxide produced during the operation of cogeneration units and gas turbines, and supplies the captured carbon dioxide directly to power-to-gas equipment to generate natural gas. The excess carbon dioxide is stored in carbon storage equipment or directly traded with external carbon markets or direct discharge.

Step 3: Based on the deterministic model of low-carbon economic dispatch of park integrated energy system considering the price-based combined heat and power demand response and V2G, introduce two-stage robust optimization to deal with the park's wind power output and electricity/heat load uncertainty, and construct a consideration A low-carbon robust economic dispatch model for park integrated energy systems based on price-based combined heat and power demand response and V2G.

Based on the deterministic model of low-carbon economic scheduling of park integrated energy system considering price-based demand response and V2G, the two-stage robust scheduling model considering the uncertainty of wind power output and load forecasting is shown in the following formula. The first stage of the model is the optimal dispatching plan for decision-making states such as the optimal dispatching of the park’s comprehensive energy system, the charging and discharging state of electric vehicles, and the transition state of price-based demand response under the basic scenario. The second stage is the dispatching plan based on the first stage On the basis of wind and solar output fluctuations and real-time load values, the output of park units, V2G and demand response loads are adjusted to ensure the safe operation of the system. Among them, the max-min sub-problem is used to identify the worst scenario that may lead to the park's maximum safety violation under uncertain conditions.

s.t.Ax+By≤b,x∈{0,1}

是目标函数的常系数向量；u为与风电、光伏出力不确定性和负荷值相关的不确定变量；ε^RO表示允许的安全阈值；A、B、C、D、E、F、G、f、b和g分别为约束常系数矩阵和向量。In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

is the constant coefficient vector of the objective function; u is the uncertain variable related to wind power, photovoltaic output uncertainty and load value; ε ^RO represents the allowable safety threshold; A, B, C, D, E, F, G, f , b and g are constrained constant coefficient matrix and vector respectively.

Step 4: Using the dual theory method, the proposed double-level maximum-minimum sub-problem of the low-carbon robust economic dispatch model of the park integrated energy system considering the price-type joint heat and power demand response and V2G is transformed into a single-level maximal problem, and the extreme value The point method is used to solve the bilinear optimization problem in the single-layer maximal problem, and finally the column and constraint generation (CCG) method is used to solve the low-carbon robust economic dispatch model of the integrated energy system of the park considering the price type combined heat and power demand response and V2G .

(4.1) The main problem of robust scheduling of the integrated energy system in the park:

由第s次迭代中的子问题中求解得到，S为迭代的总次数。The objective function of the main problem of robust scheduling is to maximize the social welfare of the park, and the constraints include basic scenario constraints and worst scenario constraints. The actual value of wind power output, photovoltaic output and load corresponding to the worst scenario

It is obtained by solving the subproblems in the sth iteration, and S is the total number of iterations.

Ax+By≤b,x∈{0,1}

(4.2) The worst-case scenario identification sub-problem of the integrated energy system of the park:

The double-layer maximum-minimum subproblem is the problem of identifying the worst scenario, finding the scenario that causes the maximum violation of the safety regulations of the system, that is, determining the specific value of the uncertainty in the worst scenario. where x ^* and y ^* are obtained from the master problem, and λ is the dual variable constrained by the linear inequality.

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ)

(4.3) Transform the double-layer maximum-minimum subproblem into a single-layer maximization problem using dual transformation:

stλ ^T E ≤ f

λ ^T F ≤ 0

λ≤0

(4.4) Use the extreme point method to solve the bilinear variable product λu problem in the single-layer maximization problem:

λu＝λ ⁰ u ^b +λ ⁺ u ⁺ +λ ^- u ^-

λ＝λ ⁰ +λ ⁺ +λ ^-

β ⁰ +β ⁺ +β ^- ＝1

-β ⁰ M≤λ ⁰ ≤β ⁰ M

-β ⁺ M≤λ ⁺ ≤β ⁺ M

-β ^- M≤λ ^- ≤β ^- M

In the formula: λ ⁰ , λ ⁺ and λ ^- are auxiliary continuous variables, β ⁰ , β ⁺ and β ^- are auxiliary 0-1 variables, corresponding to u take the upper limit u ⁺ , mean u ^b , and lower limit u ^- of the uncertain set Situation; M is a very large number.

(4.5) The specific process of CCG method to solve the proposed low-carbon robust economic dispatch model of park integrated energy system considering price-based demand response and V2G:

Step a: Let the iteration counter s=0, and set the maximum value ε ^RO that the system allows to violate safety regulations;

Step b: Solve the main problem. If there is a solution, obtain the decision state x such as the start-stop state of the system unit and the unit output arrangement y, and proceed to step c; otherwise, stop the iteration and output no solution;

Step c: According to the x and y obtained in step b, solve the maximum and minimum sub-problems, and find the wind power output and load value in the worst scenario that may cause the maximum possible violation of safety regulations;

向主问题中增加如下式所示的CCG约束，返回步骤b。Step d: If the maximum possible safety violation value obtained in step c is less than ε ^RO , then x and y are the final optimization scheme and the iteration is stopped; otherwise, let s=s+1, according to the worst value obtained in step c Wind power, photovoltaic output value and load value in the scenario

Add the CCG constraint shown in the following formula to the main problem, and return to step b.

f ^T v _s ≤ε ^RO

Step 5: Input park comprehensive energy system data, equipment parameters, operating parameters, etc., and use the commercial solver GUROBI to solve the park comprehensive energy system low-carbon robust economic dispatch model considering price-based combined heat and power demand response and V2G, and obtain the park comprehensive Low-carbon economy robust dispatch optimization results for energy systems.

The park comprehensive energy system data also includes the specific composition of the park comprehensive energy system and the electricity-gas-heat energy flow topology, and the park comprehensive energy system equipment parameters include fans, photovoltaic cells, combined heat and power units, gas turbines, electric boilers, P2G , gas storage equipment, heat storage equipment, electric vehicles, carbon capture equipment, and carbon storage equipment, the quantity, capacity, and output/charging and discharging power upper and lower limits. The gas purchase price of the upper-level gas network, the carbon transaction price, various operating parameters of the above-mentioned equipment, the price-based combined heat and power demand response ratio, and electric heating load forecast data.

The effects of the present invention will be described in detail below through specific examples.

(1) Calculation example introduction

An example analysis of the low-carbon robust economic scheduling of the park's comprehensive energy system is carried out for the composition of the park's comprehensive energy system as shown in Figure 4. The park includes fans, photovoltaic cells, cogeneration units, power-to-gas equipment, electric boilers, one gas storage equipment, one heat storage equipment, two gas turbines, and 30 electric vehicles. The test tool uses Matlab2020b programming software and GUROBI9.1 commercial solver.

(2) Example scenario description

In order to verify the effectiveness of the low-carbon robust economic dispatch model of the park integrated energy system considering the price-based combined heat and power demand response and V2G, the following calculation examples 1-9 are set; Calculations 10-12.

Calculation example 1: Deterministic scheduling without considering V2G and demand response;

Calculation example 2: Consider the deterministic scheduling of V2G;

Calculation example 3: Considering the deterministic dispatch of V2G and combined heat and power demand response;

Calculation example 4: Consider the carbon trading mechanism on the basis of calculation example 1;

Calculation example 5: Consider the carbon trading mechanism on the basis of calculation example 2;

Calculation example 6: Consider the carbon trading mechanism on the basis of calculation example 3;

Calculation Example 7: Robust scheduling based on Calculation Example 4;

Calculation example 8: Robust scheduling based on calculation example 5;

Calculation example 9: Robust scheduling based on calculation example 6;

Calculation example 10: On the basis of calculation example 9, change the price of carbon emission rights to 1.2$/kg;

Calculation example 11: On the basis of calculation example 9, change the price of carbon emission rights to 12$/kg;

Calculation Example 12: Based on Calculation Example 9, change the price of carbon emission rights to 120$/kg.

(3) embodiment result analysis

Table 1 shows the cost/benefit comparison of low-carbon economy deterministic dispatching examples 1-6 of the integrated energy system in the park, where the cost is a positive value and the benefit is a negative value. It can be concluded that considering V2G and combined heat and power demand response can significantly reduce the total cost of park operation, which is conducive to the economic operation of the system. After the introduction of the carbon trading mechanism, although the transaction cost of carbon emission rights increases, the system reduces the purchase of electricity from the main network with high carbon emission intensity. The joint effect of V2G, joint heat and power demand response and carbon trading mechanism promotes the low-carbon economic operation of the park's comprehensive energy system.

Table 1 Cost/benefit ($) of calculation examples 1-6

Figure 5 and Figure 6 are the net electricity load and heat load diagrams of the deterministic dispatching examples 1-3 of the park integrated energy system without considering the carbon trading mechanism, respectively. It can be seen that V2G can enhance the controllability of electric vehicles, effectively avoid charging electric vehicles during peak hours, and improve system security. The price-based combined heat and power demand response can significantly improve the flexibility of system operation and realize the "peak-shaving and valley-filling" of the park's net load.

Table 2 shows the carbon capture and carbon emission of calculation examples 1-6, which is easy to get: when considering the carbon trading mechanism, if the total carbon emission is lower than the emission quota, the remaining quota can be sold. Conversely, when the emission is greater than the emission quota, the emission quota must be purchased from other units, otherwise the emission will not be allowed. In other words, the introduction of carbon capture equipment and carbon trading mechanism can greatly reduce the carbon emissions of the park's comprehensive energy system.

Table 2 Carbon capture and carbon emissions of calculation examples 1-6 (kg)

Table 3 shows the comparison of the total cost and carbon emissions of calculation examples 4-9. It can be seen that the robust scheduling sacrifices economy and low carbon to a certain extent to deal with uncertainty and ensure the safe operation of the system. Moreover, the carbon emission of the worst scenario of robust scheduling is equal to that of the basic scenario, both of which are within an acceptable range.

Table 3 Comparison of total cost and carbon emissions of calculation examples 4-9

Figure 7 shows the changes in purchased power, combined heat and power unit output, gas turbine output, and sold power under the low-carbon robust economic dispatch of the integrated energy system of the park considering different carbon transaction prices. It can be seen from Figure 7 that with the increase in the trading price of carbon emission rights, the comprehensive energy system of the park gradually shifts from economic operation to optimization of minimum carbon emission. Prove that a reasonable pricing mechanism can significantly reduce carbon emissions.

The above is only a specific embodiment of the present invention, but it does not limit the scope of patent protection of the present invention. Anyone who uses the description of the present invention and the content of the accompanying drawings to perform equivalent changes or replacements is directly or indirectly applied to other related technical fields. , should be included within the protection scope of the present invention.

Claims (6)

1. A park integrated energy system scheduling method considering price-based demand response and V2G, characterized in that it comprises the following steps: Step 1: Model the price-based combined heat and power demand response, V2G and carbon trading respectively, taking into account the park energy balance constraints, operation constraints, heat storage/gas storage constraints and power exchange constraints with the main grid, in order to maximize social welfare as The goal is to build a deterministic model of low-carbon economic scheduling of the park's comprehensive energy system considering price-based combined heat and power demand response and V2G; Step 2: Through cogeneration units, gas turbines, carbon capture equipment, carbon storage equipment, and power-to-gas equipment, a complete park comprehensive energy system carbon utilization cycle that considers carbon emissions, carbon capture, carbon storage, carbon trading, and carbon consumption is formed. , and model the carbon flow; Step 3: Based on the deterministic model of low-carbon economic dispatch of park integrated energy system considering the price-based combined heat and power demand response and V2G, introduce two-stage robust optimization to deal with the park's wind power output and electricity/heat load uncertainty, and construct a consideration A low-carbon robust economic dispatch model for the integrated energy system of parks based on price-based combined heat and power demand response and V2G; Step 4: Transform the double-layer maximum-minimum sub-problem of the low-carbon robust economic dispatch model of the park integrated energy system considering price-type combined heat and power demand response and V2G into a single-layer maximal problem, and use the extreme point method to solve the single-layer problem The bilinear optimization problem within the extremely large problem, and finally use the column sum constraint generation method to solve the low-carbon robust economic dispatch model of the park integrated energy system considering the price-type joint heat and power demand response and V2G; Step 5: Input the park comprehensive energy system data, equipment parameters, and operating parameters, and use the commercial solver GUROBI to solve the low-carbon robust economic dispatch model of the park comprehensive energy system considering price-type joint heat and power demand response and V2G, and obtain the park comprehensive energy System low-carbon economy robust scheduling optimization results. 2. The dispatching method of park integrated energy system considering price-based demand response and V2G according to claim 1, characterized in that the low-carbon economic scheduling of park integrated energy system considering price-based combined heat and power demand response and V2G in step 1 The deterministic model is as follows: (1) Objective function:

In the formula: C ^dr is the income obtained by the price type combined heat and power demand response;

C ^o is the operating cost of the park; C ^cur is the wind/light penalty cost; C ^loss is the load loss penalty cost; t is the scheduling time; e is the electric load; h is the heat load; k is the number of segments ;

and

Respectively represent the bidding price of demand response electric load e and heat load h of segment k at time t; P _{ekt represents the electric power of demand response electric load e of segment k at time t, H hkt represents} the demand response heat of segment k The thermal power of load h at time t; C ^tran is the carbon trading price; D _t is the carbon emission quota of the park at time t;

Indicates the carbon emissions of the park at time t; C ^buy indicates the unit price of carbon purchased from the carbon market; C ^sell indicates the unit price of carbon sold to the carbon market;

Indicates the amount of carbon purchased at time t;

Indicates the amount of carbon sold at time t;

and

^respectively represent the _purchase ^price and sale _price of electricity from the industrial park to the upper grid;

is the gas purchase price;

Purchase gas power from the superior gas network for the park; r and w are the indexes of wind turbine and photovoltaic respectively;

and

Respectively represent the unit price of wind curtailment and solar curtailment penalty;

and

Respectively represent the curtailed wind power and curtailed optical power of the park at time t;

and

Respectively represent the penalty price of power loss load and the penalty price of heat loss load; v _et and v _ht represent the variables of power loss load and heat loss load respectively; (2) Constraints: (2.1) Price-type combined heat and power demand response constraints When the bidding price of demand response is less than the time-of-use electricity price, the price-type responsive load participates in the operation scheduling of the park; when the responsive load is positive, it means that the electric load is cut or transferred to other operating time, and when the responsive load is negative, it means this time Get the load shifted from other moments while increasing:

In the formula:

and

Indicates the transfer-in time and transfer-out time of electric load respectively; T _e ^on and T _e ^off represent the minimum transfer-in time and transfer-out time of electric load respectively; Y _et and Y _e,t-1 represent time t and time t-1 respectively The 0-1 variable of the electric load transfer state is 1 for the transfer out and 0 for the transfer in; P _et represents the actual electric load power of the park;

Indicates the predicted electric load power; P _ekt represents the electric power of the electric load at the kth segment t time;

Indicates that it can respond to electrical loads;

Indicates the maximum electric power of the kth section; M is a sufficiently large positive number; α _et represents the proportion of electric load that can be responded to;

Indicates the maximum electric load power at time t;

Indicates the overall reduction of electric load;

and

Respectively represent the heat load transfer-in time and transfer-out time;

and

respectively represent the minimum heat load transfer-in time and transfer-out time; Y _ht and Y _h,t-1 respectively represent the 0-1 variable of the heat load transfer state at time t and t-1, the transfer-out is 1, and the transfer-in is 0 ; H _ht represents the actual heat load power of the park;

Indicates the predicted thermal load power; H _hkt indicates the thermal power of the thermal load at the kth segment t time;

Indicates that it can respond to thermal load;

Indicates the maximum thermal power of section k; α _ht indicates the proportion of responsive thermal load;

Indicates the maximum thermal load power at time t;

Indicates the overall reduction of heat load; (2.2) V2G constraints

In the formula: l is the index of the electric vehicle;

Indicates the charging state of the electric vehicle, 1 for charging, 0 otherwise;

Indicates the discharge state of the electric vehicle, discharge is 1, otherwise it is 0;

is the sum of access time and charging and discharging time;

respectively represent the charging and discharging power of electric vehicles; P _l ^c,rate and P _l ^d,rate respectively represent the rated charging efficiency and discharging power of electric vehicles;

Indicates the 0-1 variable of the access time of the electric vehicle, the access time is 1, and the rest of the time is 0; M indicates a sufficiently large positive number;

Indicates the state of charge of the electric vehicle battery;

Indicates the initial state of charge of the electric vehicle;

Indicates the state of charge of the battery at time t-1 of the electric vehicle; η _l ^ev,c and η _l ^ev,d represent the charging efficiency and discharge efficiency of the electric vehicle, respectively;

Indicates the battery capacity of electric vehicles;

Indicates the departure time of the electric vehicle, the departure time is 1, and the rest of the time is 0;

Indicates the state of charge of the battery when the electric vehicle leaves;

and

Respectively represent the lower limit and upper limit of the battery state of charge; (2.3) Carbon capture and storage constraints

In the formula:

Indicates the carbon emissions of the park at time t; p and q are the indexes of CHP and gas turbine respectively;

Indicates the carbon emission of the pth CHP at time t;

Indicates the carbon emission of the qth gas turbine at time _t ;

Indicates the carbon emissions generated by purchasing electricity from the upper-level grid; i is the index of the carbon capture unit;

Indicates the amount of carbon dioxide captured by the i-th carbon capture unit;

and

are the carbon storage and output of carbon storage equipment, respectively;

Indicates the amount of carbon purchased in the park at time t; m is the index of P2G;

Indicates the carbon consumption of the mth P2G at time t;

Indicates the amount of carbon sold in the park at time t;

is the carbon capture rate;

and μ ^upper represent the carbon emission intensity of CHP, gas turbine and main network respectively;

and

respectively represent the output of CHP and gas turbine at time t;

Indicates the amount of carbon dioxide required to generate unit power of natural gas;

Indicates the power-to-gas efficiency of the mth P2G;

Indicates the power consumption of the mth P2G at time t; L ^HANG indicates the low calorific value of natural gas;

Indicates the carbon storage capacity of the carbon storage equipment;

Indicates the carbon storage capacity of the carbon storage equipment at time t-1; η ^s is the carbon storage loss coefficient; C ^s,min and C ^s,max respectively represent the minimum carbon storage capacity and maximum carbon storage capacity of the carbon storage equipment; Min ^{, min} and M ^in,max represent the minimum carbon storage and maximum carbon storage of carbon storage equipment; M ^out,min and M ^out,max are the minimum and maximum carbon output of carbon storage equipment; M ^b,max represent The maximum amount of purchased carbon in the park; M ^s,max represents the maximum amount of carbon sold in the park;

Indicates the power consumption of the i-th carbon capture unit at time t; θ is the energy consumption per unit of carbon dioxide;

Indicates the start-stop status of the carbon capture equipment, 1 for power-on and 0 for power-off;

Indicates the fixed energy consumption of carbon capture equipment; (2.4) Energy balance constraints

In the formula:

and

Respectively represent the output of the rth wind turbine and the wth photovoltaic unit at time t; P _et is the actual electric load after considering demand response at time t; n is the index of the electric boiler;

Indicates the power consumption of the nth electric boiler at time t;

is the gas power generated by the m-th P2G at time t;

and

are the gas power stored and released by the gas storage device at time t, respectively;

and

are the gas power consumed by CHP and gas turbine respectively; η ^heat is the thermal energy utilization rate of the park;

and

are the heat production power of CHP and electric boiler, respectively;

and

are the heat power stored and released by the heat storage device, respectively; (2.5) Power exchange constraints with the main network P ^in,min ≤P _t ⁱⁿ ≤P ^in,max P ^out,min ≤P _t ^out ≤P ^out,max

In the formula: P ^in,min and P ^in,max respectively represent the minimum and maximum electric power purchased from the main network; P ^out,min and P ^out,max are the minimum and maximum electric power sold to the main network; G ^in,min and G ^in,max are the minimum and maximum gas power purchased from the main network, respectively; (2.6) Abandoned wind and light constraints and lost load constraints

In the formula:

and

Respectively, the allowable wind curtailment ratio, solar curtailment ratio, power loss load ratio and heat loss load ratio; (2.7) Operational constraints (2.7.1) CHP operation constraints

In the formula:

and

Respectively, the heating coefficient and flue gas recovery rate of the CHP internal bromine refrigerator;

is the power generation efficiency of the CHP internal micro-combustion engine;

is the heat loss rate;

and

are the start-up cost and shutdown cost of CHP, respectively;

and

Respectively, CHP single start-up cost and shutdown cost;

and

Respectively, the switching status of CHP at time t and time t-1, 1 for power-on and 0 for power-off;

and

Respectively, the minimum electric power and maximum electric power of CHP output;

is the contribution of CHP at time t-1;

and

Respectively, the up-slope rate and the down-slope rate of CHP;

Respectively, the continuous power-on and power-off time of CHP;

Respectively, the minimum power-on time and the minimum power-off time of CHP; (2.7.2) Gas turbine operating constraints

In the formula: F( ) is the heat rate curve of the gas turbine;

are the start-up cost and shutdown cost of CHP, respectively;

It is the minimum output value of the gas turbine;

is the on/off state of the gas turbine at time t, 1 for power on and 0 for power off;

is the gas consumption increment of the gas turbine in the k-th section;

is the electric power of the gas turbine at the kth segment at time t; (2.7.3) P2G operation constraints

In the formula:

is the power-to-gas efficiency of P2G;

and

Respectively, the minimum gas production power and maximum gas production power of P2G; (2.7.4) Electric Boiler Operation Constraints

In the formula:

is the electric heating efficiency of the electric boiler;

Respectively, the minimum heating power and maximum heating power of the electric boiler; (2.7.5) Operation constraints of gas storage and heat storage equipment

In the formula:

and

are the gas storage power and gas discharge power of the gas storage equipment; G ^GS,in,max and G ^GS,out,max are the maximum gas storage power and maximum gas discharge power of the gas storage equipment;

and

are the gas storage capacity of the gas storage equipment at time t and t-1 respectively; η ^CGS , η ^GS,in and η ^GS,out are the self-consumption rate, gas storage efficiency and gas release efficiency of the gas storage equipment, respectively;

and

are the heat storage power and heat release power of the heat storage equipment, respectively; H ^HS,in,max and H ^HS,out,max are the maximum heat storage power and maximum heat release power of the heat storage equipment, respectively;

and

are the heat storage capacity of the heat storage equipment at time t and t-1, respectively; η ^CHS , η ^HS,in and η ^HS,out are the self-consumption rate, heat storage efficiency, and heat release efficiency of the heat storage equipment, respectively; (2.8) General vector form Write the above deterministic optimal scheduling model in general vector form:

s.t.Ax+By+Cv≤b,x∈{0,1} In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

and

is the constant coefficient vector of the objective function; A, B, C and b are constrained constant coefficient matrix and vector, respectively. 3. The park integrated energy system scheduling method considering price-based demand response and V2G according to claim 1, characterized in that, in step 2, the integrity of carbon emissions, carbon capture, carbon storage, carbon trading, and carbon consumption is considered. The carbon utilization cycle of the comprehensive energy system in the park is as follows: Carbon capture equipment captures carbon dioxide produced during the operation of cogeneration units and gas turbines, and supplies the captured carbon dioxide directly to power-to-gas equipment to generate natural gas. The excess carbon dioxide is stored in carbon storage equipment or directly traded with external carbon markets or direct discharge. 4. The park integrated energy system scheduling method considering price-based demand response and V2G according to claim 1, characterized in that in step 3, the park integrated energy system considering price-based combined heat and power demand response and V2G is low-carbon and robust The details of the economic dispatch model are as follows: Based on the deterministic model of low-carbon economic scheduling of park integrated energy system considering price-based demand response and V2G, the two-stage robust scheduling model considering the uncertainty of wind power output and load forecasting is shown in the following formula; the first part of the model The first stage is the optimal dispatching scheme for decision-making states such as the optimal dispatching of the park’s comprehensive energy system, the charging and discharging state of electric vehicles, and the transition state of price-based demand response under the basic scenario. The second stage is based on the dispatching plan of the first stage, according to the Output fluctuations and load real-time values adjust park unit output, V2G, and demand response loads to ensure safe operation of the system; among them, the maximum and minimum sub-problems are used to identify the worst scenario that may lead to the maximum safety limit of the park under uncertain conditions;

s.t.Ax+By≤b,x∈{0,1}

In the formula: x represents the start-stop state of each unit, the charging and discharging state of electric vehicles, and the transfer-in and transfer-out status of price-type combined heat and power demand response; y represents the remaining dispatching power of the system; v represents the load loss;

is the constant coefficient vector of the objective function; u is the uncertain variable related to wind power, photovoltaic output uncertainty and load value; F(x,y) is the function of x and y; ε ^RO represents the allowable safety threshold; A, B, C, D, E, F, G, f, b, and g are constrained constant coefficient matrices and vectors, respectively. 5. The park integrated energy system dispatching method considering price-type demand response and V2G according to claim 1, characterized in that, in step 4, the dual transformation, extreme point method and CCG method are used to consider the price-type combined heat and power demand The process of solving the low-carbon robust economic dispatch model of the integrated energy system of the park in response to and V2G is as follows: (1) The main problem of robust scheduling of park integrated energy system: The objective function of the main problem of robust scheduling is to maximize the social welfare of the park, and the constraints include basic scenario constraints and worst scenario constraints; the worst scenario corresponds to wind power output, photovoltaic output and actual load values

It is obtained by solving the subproblems in the sth iteration, and S is the total number of iterations;

Ax+By≤b,x∈{0,1}

In the formula, v _s , z _s and

are respectively the sth iteration values of load loss, system continuous variables and uncertain variables; (2) The worst scenario identification sub-problem of the integrated energy system of the park: The double-layer maximum-minimum sub-problem is the problem of identifying the worst scenario, finding the scenario that causes the maximum violation of the safety regulation value of the system, that is, determining the specific value of the uncertain quantity in the worst scenario; among them, x ^* and y ^* are determined by the main problem Obtained, λ is the dual variable of the linear inequality constraint;

Ez+Fv+Gu≤g-Cx ^* -Dy ^* :(λ) (3) Transform the double-layer maximum-minimum subproblem into a single-layer maximization problem using dual transformation:

stλ ^T E ≤ f λ ^T F ≤ 0 λ≤0 (4) Using the extreme point method to solve the bilinear variable product λu problem in the single-layer maximization problem: λ _u ＝λ ⁰ u ^b +λ ⁺ u ⁺ +λ ^- u ^- λ＝λ ⁰ +λ ⁺ +λ ^- β ⁰ +β ⁺ +β ^- ＝1 -β ⁰ M≤λ ⁰ ≤β ⁰ M -β ⁺ M≤λ ⁺ ≤β ⁺ M -β-M≤λ ^- ≤β ^- M In the formula: λ ⁰ , λ ⁺ and λ ^- are auxiliary continuous variables, β ⁰ , β ⁺ and β ^- are auxiliary 0-1 variables, corresponding to u take the upper limit u ⁺ , mean u ^b , and lower limit u ^- of the uncertain set situation; M is a very large number; (5) The specific process of CCG method to solve the proposed low-carbon robust economic dispatch model of park integrated energy system considering price-based demand response and V2G: Step a: Let the iteration counter s=0, and set the maximum value ε ^RO that the system allows to violate the security regulations; Step b: Solve the main problem. If there is a solution, obtain the decision state x such as the start-stop state of the system unit and the unit output arrangement y, and proceed to step c; otherwise, stop the iteration and output no solution; Step c: According to the x and y obtained in step b, solve the maximum and minimum sub-problems, and find the wind power output and load value in the worst scenario that may cause the maximum possible violation of safety regulations; Step d: If the maximum possible safety violation value obtained in step c is less than ε ^RO , then x and y are the final optimization scheme and the iteration is stopped; otherwise, let s=s+1, according to the worst value obtained in step c Wind power, photovoltaic output value and load value in the scenario

Add the CCG constraint shown in the following formula to the main problem, and return to step b; f ^T v _s ≤ε ^RO

In the formula, v _s and z _s are respectively the loss of load and the sth iteration value of the continuous variable of the system. 6. The park integrated energy system scheduling method considering price-based demand response and V2G according to claim 1, characterized in that, the park integrated energy system data in step 5 also includes the specific composition of the park integrated energy system and the electricity-gas- Thermal energy flow topology, the park’s comprehensive energy system equipment parameters include fans, photovoltaic cells, cogeneration units, gas turbines, electric boilers, P2G, gas storage equipment, heat storage equipment, electric vehicles, carbon capture equipment, and carbon storage equipment The quantity, capacity, and upper and lower limits of output/charging and discharging power. The operating parameters of the park’s comprehensive energy system include electricity purchase prices from the upper-level power grid, gas purchase prices from the upper-level gas network, carbon trading prices, and various operating parameters and price types of the above-mentioned equipment. Combined heat and power demand response ratio and electric heat load forecast data.

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